{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ordered-correction",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from matplotlib.font_manager import FontProperties\n",
    "import pandas as pd\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "metric-bidder",
   "metadata": {},
   "outputs": [],
   "source": [
    "load_model_file = tf.keras.models.load_model('icds_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "appreciated-melissa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测输入值：     FS    SN    Cvm  Cvb  Thin40  Thin50  Thick50  Neps140  Neps200    H\n",
      "0  100  18.5  12.82  3.2      83       1     39.5      282       80  5.1\n",
      "预测结果： [[148.18108     4.44105     0.9283899  29.295898   82.505295    9.1711\n",
      "   32.87486    76.047424    8.325789 ]]\n"
     ]
    }
   ],
   "source": [
    "prediction_set_data = pd.DataFrame([[14,18.5,12.82,3.2,83,1,39.5,282,80,5.1]],columns=['FS','SN','Cvm','Cvb','Thin40','Thin50','Thick50','Neps140','Neps200','H'])\n",
    "print(\"预测输入值：\",prediction_set_data)\n",
    "prediction_set_data_result = load_model_file.predict(prediction_set_data)\n",
    "print(\"预测结果：\",prediction_set_data_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "impressive-volleyball",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       dtype=float32),\n",
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       "       dtype=float32),\n",
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       "       dtype=float32),\n",
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       "          5.25524735e-01,  1.05605090e+00, -3.75226177e-02],\n",
       "        [ 2.57402277e+00, -1.21312127e-01,  4.13767993e-02,\n",
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       "         -2.07429705e-03,  1.08141601e+00,  3.38932902e-01],\n",
       "        [ 2.68912697e+00, -1.17055058e-01, -1.47960512e-02,\n",
       "         -1.57359987e-01,  1.22662282e+00,  7.74001837e-01,\n",
       "          2.77008444e-01,  1.49512291e+00, -4.06175777e-02],\n",
       "        [ 3.07140088e+00, -5.59369624e-01,  1.07112359e-02,\n",
       "          2.10998923e-01,  1.55629134e+00,  2.95716077e-01,\n",
       "         -2.22579930e-02,  1.13878548e+00, -5.41758060e-01],\n",
       "        [ 2.67296433e+00,  7.25810304e-02,  1.98596627e-01,\n",
       "          4.78745848e-01,  1.76688313e+00,  3.44845682e-01,\n",
       "          5.13025820e-01,  1.38459587e+00,  4.69457567e-01]], dtype=float32),\n",
       " array([2.3833027 , 0.1530532 , 0.01698027, 0.63765484, 1.5376006 ,\n",
       "        0.03047785, 0.6288036 , 1.1499131 , 0.25167495], dtype=float32)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = load_model_file.get_weights()\n",
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dense-collaboration",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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